Pressure-Swing Distillation for Separating Pressure-Insensitive

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Pressure-Swing Distillation for Separating PressureInsensitive Minimum Boiling Azeotrope Methanol/Toluene via Introducing a Light Entrainer: Design and Control Ye Li, and Chunjian Xu Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b04939 • Publication Date (Web): 22 Mar 2017 Downloaded from http://pubs.acs.org on March 30, 2017

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Industrial & Engineering Chemistry Research

Pressure-Swing Distillation for Separating Pressure-Insensitive Minimum Boiling Azeotrope Methanol/Toluene via Introducing a Light Entrainer: Design and Control Ye Li, Chunjian Xu* School of Chemical Engineering and Technology, Chemical Engineering Research Center, State Key Laboratory of Chemical Engineering and Collaborative Innovation Center of Chemical Science and Engineering, Tianjin University, Tianjin 300072, China Tel: +86 022-27404440. Fax: +86 022-27404440. E-mail: [email protected].

ABSTRACT: In this paper, pressure-swing distillation (PSD) is applied to separate pressure-insensitive minimum boiling azeotrope methanol/toluene via introducing chloroform as a light pressure-swing entrainer. The feasibility of this process is verified by analyzing the residue curve maps. Both partially and fully heat-integrated PSD processes are implemented. The steady-state designs of these processes are optimized basing on total annual cost (TAC). In this extended PSD process, the high-pressure column (HPC) serves as the homogeneous azeotropic distillation column, while the low-pressure column (LPC) serves as the entrainer recovery column. It is found that the HPC has large temperature slope in the stripper section while with relatively flat trend above. This unique phenomenon makes it beneficial to reduce the energy consumption by preheating the feed and recycle stream using low-grade heat. Both conventional and partially heat-integrated extractive distillation processes with aniline as the entrainer are also implemented to compare the economy with these PSD processes. The results reveal that TAC of the partially heat-integrated extractive distillation process is higher than the conventional

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one. It also reveals that the partially and fully heat-integrated PSD processes have almost similar TAC and energy consumption, while the optimal PSD process has 5.39% reduction of TAC and 8.32% energy saving compared to the optimal conventional extractive distillation process. The dynamic controllability of this extended PSD is investigated by introducing ±20% disturbances of feed flowrate and composition for both partially and fully heat-integrated processes. The results reveal that the enthalpic state of the feed has crucial influence on the dynamic controllability, as it can influence the temperature profile and composition profiles of HPC greatly. Although the economic optimal PSD process shows weak controllability, robust control can be achieved under another superior design. Key words: pressure-insensitive, minimum boiling azeotrope, methanol/toluene, pressure-swing distillation, steady-state design, dynamic controllability 1. INTRODUCTION Pressure-swing distillation (PSD) has been widely used for the separation of pressure-sensitive azeotropes. Many researchers have investigated the comparison between PSD and extractive distillation (ED) on both steady-state design and dynamic performance.1-7 PSD always features partial or full integration, which can largely reduce energy consumption.8-10 Semicontinous and batch PSD are also well studied.11-13 Modla et al.14 investigated the separation of ternary homoazeotropic mixtures by pressure swing systems and they proposed a classification for it. Recently, Luyben15, 16 and Li17 have applied PSD to separate maximum boiling azeotropes. However, PSD is usually not able to separate pressure-insensitive azeotropes. Actually, PSD can be extended to separate pressure-insensitive azeotropes. Knapp18 proposed a novel PSD process for separating pressure-insensitive azeotrope with a suitable entrainer. These


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entrainers must satisfy at least one of these principles as below: (1) The entrainer forms no new azeotropes at atmospheric pressure, but when the pressure is increased (decreased), new azeotrope(s) appear which move rapidly with changing pressure; (2) The entrainer forms one or more new azeotropes whose composition(s) change rapidly with pressure; (3) The entrainer forms one or more new azeotropes at atmospheric pressure, but they disappear as the pressure is increased (decreased). They demonstrated the feasibility of this process through investigating the dehydration of ethanol using acetone as the pressure-swing entrainer. The schematic flowsheet of this process is depicted in Figure 1a. The heavy products ethanol and water withdraw from the bottoms, while the pressure-sensitive ternary azeotrope recycles. Though high purity products can be obtained, this extended PSD was shown less economical than ED, basing on both total annual cost (TAC) and energy consumption. Li19 applied this extended PSD to separate pressure-insensitive maximum boiling azeotrope phenol/cyclohexanone using acetophenone as a heavy entrainer. The schematic flowsheet of this process is depicted in Figure 1b. The light products phenol and cyclohexanone withdraw as the distillates, while the pressure-sensitive heavy binary azeotrope recycles. The results showed that high purity products can be obtained and good controllability can be achieved. Though the research conducted by Knapp showed inferior economy of this extended PSD compared with ED, the application for other systems is still unknown. Furthermore, different azeotropic systems may have different PSD sequence. And to our best knowledge, the dynamic controllability of this extended PSD for separating minimum boiling azeotrope has not been investigated in the open literature. Methanol and toluene form pressure-insensitive minimum boiling azeotrope, of which the



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separation has been investigated using ED with heavy entrainer and intermediate entrainer.20-23 In this study, the separation of methanol/toluene is investigated using this extended PSD with chloroform as a light entrainer. Both partially and fully heat-integrated PSD processes are implemented. The steady-state designs of these processes are optimized basing on TAC. Meanwhile, both conventional and partially heat-integrated ED processes with aniline as the entrainer are also implemented to compare the economy with these extended PSD processes. Furthermore, the dynamic controllability of this extended PSD is investigated using conventional temperature control structure. The crucial influence of the enthalpic state of the feed on the dynamic controllability is discovered and analyzed. 2. STEADY-STATE DESIGN 2.1. Thermodynamic Model and Entrainer Selection. In this study, the UNIQUAC model is selected and the built-in binary interaction parameters in Aspen Plus are used to predict the vapor-liquid equilibrium of the system. The details can be found in Supporting Information. Methanol and toluene form a pressure-insensitive minimum boiling azeotrope with 88.43 mol% methanol and boiling point of 63.76 at 1 atm. The heuristic entrainer screening procedure proposed by Foucher24 was employed to select the entrainer. By searching the Azeotropic Data25, considering the principles proposed by Knapp, we found that chloroform would be the potential pressure-swing entrainer, which forms pressure-sensitive minimum boiling azeotrope with methanol. 2.2. Residue Curve Maps. Residue curve map (RCM) is an important and useful method in the conception design of


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distillation. Figure 2a, b shows the RCMs of methanol/toluene/chloroform system at 1 atm and 10 atm. There exists a distillation boundary from the methanol/chloroform azeotrope to the methanol/toluene azeotrope. The methanol/toluene azeotrope is pressure-insensitive, whereas the methanol/chloroform azeotrope moves toward the chloroform vertex with decreasing column pressure. Figure 2c analyzes the feasibility of this process, which depicts the material balance relationship basing on lever-arm principle. In this process, the high-pressure column (HPC) serves as the homogeneous azeotropic distillation column, while the low-pressure column (LPC) serves as the entrainer recovery column. In the HPC, the total feed point F1 is the mixture of the fresh feed F and the recycle stream S (D2). F1 is separated into pure toluene product B1 and distillate stream D1. The distillate stream D1 is essentially a binary methanol/chloroform mixture which is close to the azeotrope at 10 atm. Then, in the LPC, D1 is separated into pure methanol product B2 withdrawn from the bottom and the near azeotrope overhead distillate of methanol/chloroform severing as the recycle stream S (D2). To balance the tiny loss of the entrainer, a makeup stream of chloroform (Makeup) is added into the system. Note that the distillation boundary showed in Figure 2b is crossed with decreasing operating pressure (OP). Therefore, the purified toluene can be obtained from HPC with the purified methanol withdrawn from LPC. Thus, the separation for this pressure-insensitive azeotrope methanol/toluene is feasible with chloroform as the pressure-swing entrainer. 2.3. Optimization. The feed composition, flowrate and temperature are selected as the same as Molda20 and Luyben23, with composition of 50 mol% methanol, flowrate of 100 kmol/h and temperature of 50. The product purities of methanol and toluene are both specified at 99.9 mol%.



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In this work, TAC suggested by Douglas27 including annual capital costs and operating costs, is used as the objective function to be minimized to screen the optimal design among feasible alternatives. It can be expressed by the equations TAC (k$/year) = OC + ir·FCI where FCI is the fixed capital investment; ir is the fixed capital recovery rate applied to FCI and it is assumed to be 0.3 here; OC is the operating cost, mostly utility consumption (stream, cooling water, and electricity). The costs of the entrainer and entrainer makeup are not included in OC because they are much lower than the costs of the heat duties. Major pieces of equipment for this process are the two column vessel (including column internals) and heat exchangers (condensers, reboiler, and cooler). Small items such as reflux drums, pumps, valves, and pipes are usually not considered at the conceptual design stage because of their lower costs compared with the costs of the column vessel and heat exchangers.26 The Tray Sizing section in Aspen Plus is employed to size the column with a sieve plate. As the diameters of these columns are small, we specify the tray spacing as 0.3 m for all columns. The heat transfer area for the heat exchangers is calculated by dividing the heat duty by the product of the overall heat transfer coefficient and a differential temperature driving force. The overall heat transfer coefficients are assumed to be 0.852 kW/(K·m2) for the condensers and heat exchangers and 0.568 kW/(K·m2) for the reboilers, respectively.26 All the major equipment costs are estimated by Douglas formulas.27 The details can be found in Supporting Information. The utility costs are calculated in terms of the heat duties of reboilers, condensers and heat exchangers. The utility prices taken from CAPCOST28 can be found in Supporting Information.


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In this study, both partially and fully heat-integrated PSD processes are implemented. For the purpose of simplicity, we denote partially heat-integrated PSD as PHI-PSD and fully heat-integrated PSD as FHI-PSD. Similarly, C-ED and PHI-ED denote conventional extractive distillation and partially heat-integrated extractive distillation, respectively. Preliminary simulations of the PSD process show large temperature increasing in the stripper section of the HPC, while with relatively flat trend in the upper section. This inspires us to preheat the feed and the recycle stream using low-grade heat rather than the high-grade heat of the reboiler, which is aimed to reduce the reboiler duty of HPC, furthermore to reduce the energy consumption. The feed is preheated to 174 which can guarantee the use of medium pressure steam, and the recycle stream is preheated to saturated vapor. The schematic flowsheet of this process is depicted in Figure 3. 2.3.1. Optimization of PHI-PSD. Knapp18 thought that the compositions of the streams connecting the HPC and LPC are dominant optimization variables, because they control the recycle stream to fresh feed ratio. It can be easily verified through analyzing the RCMs. When the distillate composition of HPC is given, the material balance relationship between recycle stream and the distillate composition of LPC can be expressed as below: ∙ 50 mol% + ∙ , , = ∙ , , where xS,MeOH and xS,CHCl3 are the mole fractions of methanol and chloroform of the recycle stream S, respectively; xD1,MeOH and xD1,CHCl3 are the mole fractions of methanol and chloroform of HPC distillate D1, respectively. Note that in the HPC, as the distillate composition can’t cross the distillation boundary, the



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azeotropic point will be the limiting value of the distillate composition. And in the LPC, the azeotropic point will also be the limiting value of the distillate composition. Thus, under a reasonable composition of D1, there exists one minimum recycle stream flowrate due to the limiting value of the recycle stream composition. As the composition of D1 is a crucial variable, in the optimization of this PSD process, it is selected as one of the optimization variables. For each optimization, the distillate composition of HPC is set at 0.01, 0.02, 0.04, 0.06, 0.08 away from the azeotrope basing on methanol mole fraction. Furthermore, under a given composition of D1, the composition of recycle stream is determined by the recycle stream flowrate. When the recycle stream flowrate is set as one optimization variable, its composition is fixed. Therefore, in the optimization of this process, the reflux ratio and reboiler duty of each column are varied to meet the design specifications of the distillate composition of HPC and LPC, and also both product purities. Furthermore, as the differential OP of HPC and LPC may lead to different optimal results, considering the complete optimization of both OPs of HPC and LPC is time-consuming, for simplicity purposes, we fix the OP of LPC at 0.8 atm, which can guarantee the use of cooling water in the condenser, and optimize the OP of HPC. In summary, under the OP of LPC (P2) fixed, the OP of HPC (P1), the total stage numbers of LPC (NT2) and HPC (NT1), the distillate composition of HPC (xD1), the flowrate of the recycle stream (S) and the feeding locations(NF1, NF2, NFS) of the feed stream are the optimization variables to minimize the TAC. The reflux ratios of HPC (RR1) and LPC (RR2), and reboiler duty of HPC (QR1) and LPC (QR2) are varied to meet the specifications of the composition of D1, D2, B1 and B2.


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The iteration procedure of the optimization can be found in Supporting Information. To select the optimal OP of HPC, three case studies are implemented with the OP of LPC fixed at 0.8 atm. The optimal steady-state designs of PHI-PSD with HPC operating at 8, 10 and 12 atm are compared with details listed in Table 1. The optimal flowsheet of PHI-PSD with OP of HPC set at 10 atm and 12 atm are shown in Figure 4 and Figure 5, respectively. 2.3.2. Optimization of FHI-PSD. In the optimization of FHI-PSD, the reboiler duty of LPC is equal to the condenser duty of HPC. The iteration procedure of the optimization can be found in Supporting Information. The optimal steady-state designs of FHI-PSD with HPC operating at 8, 10 and 12 atm are compared with details listed in Table 2. The optimal flowsheet of FHI-PSD with OP of HPC set at 10 atm and 12 atm are shown in Figure 6 and Figure 7, respectively. 2.3.3. Optimization of C-ED. For C-ED implemented by Ma21, we re-optimized this process basing on their initial parameters and iteration procedure. The optimal flowsheet is shown in Figure 8. 2.3.4. Optimization of PHI-ED. In the optimal flowsheet of C-ED, the condenser duty of entrainer recovery column (ERC) is much lower than the reboiler duty of extractive distillation column (EDC), so fully heat-integrated ED process is not realistic. However, PHI-ED can be implemented to compare the economy with C-ED. In the optimization of PHI-ED, there are several restrictive conditions.26 Firstly, the reflux drum temperature of EDC must be at least 20 higher than the temperature of available cooling water so the



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use of cooling water in the condenser is available. Secondly, a reasonable temperature difference between overhead vapor of ERC and reboiler of EDC is higher than 20 . Thirdly, the temperature difference between the reboiler and the high pressure steam in ERC must be reasonable. To obtain a lower reboiler temperature of ERC, the OP of EDC is set at 0.548 atm, offering the reflux drum temperature of EDC 50 . The OP of ERC is set to guarantee the overhead vapor temperature of ERC is 20 higher than the reboiler temperature of EDC. Note that once the flowrate of entrainer recycle stream is given, the operation pressure of ERC can be estimated. The iteration procedure of the optimization can be found in Supporting Information. The optimal flowsheet is shown in Figure 9. 2.3.5. Comparison and Analysis. The overall economic comparison of PSD and ED is listed in Table 3. It is obviously that the results of PHI-PSD process are similar with that of FHI-PSD process. Although the OC of PHI-ED is lower than that C-ED, the large FCI of PHI-ED leads to larger TAC than C-ED. The optimal result of PSD is PHI-PSD flowsheet with OP of HPC at 12 atm, which has 5.39% reduction of TAC and 8.32% energy saving compared to C-ED. Initially, the use of pre-heater is just aimed to reduce the reboiler duty of HPC. However, in the simulations, we found that there exists a critical vapor fraction of feed that can largely change the temperature profile and composition profiles of HPC. For the optimal PHI-PSD flowsheet with OP of HPC at 10 atm, the vapor fraction of feed is 0.85. The temperature profile and composition profiles of HPC are shown in Figure 10. When the vapor fraction of feed is set at 0.8, the corresponding temperature profile and composition profiles of HPC are


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shown in Figure 11. It can be seen that the temperature profiles and composition profiles are very different. When the vapor fraction of feed increases, the stripper section lack sufficient ability to separate toluene. Thus toluene is separated through some other stages upper the stripper section, which leads to the large change of temperature profile and composition profiles of HPC. This phenomenon can be confirmed for the case of PHI-PSD with OP of HPC at 12 atm. The vapor fraction of feed of the optimal PHI-PSD flowsheet with OP of HPC at 12 atm is 0.72. The corresponding temperature profile and composition profiles of HPC are shown in Figure 12. When the vapor fraction of feed is set at 0.8, the temperature profile and composition profiles of HPC are shown in Figure 13. It can be seen that there exists two styles of temperature profile. The ‘stair-step’ style corresponds to larger vapor faction of feed. The ‘reversed L’ style features large slope near the bottom with relative flat trend above, corresponding to smaller vapor faction of feed. For both optimal PHI-PSD and FHI-PSD processes with OP of HPC at 10 atm, the temperature profiles of HPC show ‘stair-step’ style. For both optimal PHI-PSD and FHI-PSD processes with OP of HPC at 12 atm, the temperature profiles of HPC show ‘reversed L’ style. From the standpoint of economic optimization, for PHI-PSD and FHI-PSD processes with OP of HPC at 10 atm, the design with the vapor fraction of feed at 0.8 is inferior to that with the vapor fraction equal to 0.85. Meanwhile, for PHI-PSD and FHI-PSD processes with the OP of HPC at 12 atm, the vapor fraction of feed at 0.8 can’t be achieved considering that the limitation of outlet temperature of feed pre-heater is 174 . However, these different shapes of temperature profiles will have large influence on dynamic controllabilities of these processes. 3. DYNAMIC CONTROL



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In this section, dynamic performances of three PSD processes are investigated. Firstly, the dynamic controllability of PHI-PSD process with OP of HPC at 12 atm is explored. Secondly, both PHI-PSD and FHI-PSD flowsheet with OP of HPC at 10 atm are implemented to explore the dynamic performances. Conventional temperature control strategies are applied to all of these processes. The sizes of equipment are necessary to convert a steady-state simulation to dynamic one. The “Tray Sizing” section in Aspen Plus is used to define the sizes of equipment such as decanter, reflux drum and column base. The column bases and reflux drums are sized to provide 10 min of holdup when at the 50% liquid level. Pumps and valves are inserted to provide adequate pressure drops so that the flow sheet is fully pressure-driven and the valves have good range ability. All valves are specified to have pressure drops of 300 kPa with the valve half open at the design flowrate. The sensitive tray is selected by “slope criterion” suggested by Luyben.26 3.1. Dynamic Control of PHI-PSD with OP of HPC at 12 atm. The temperature profiles of HPC can be seen in Figure 12a. The temperature profiles of LPC are shown in Figure 14. The location of the stage in HPC with the largest slope is the 38th. The location of the stage in LPC with the largest slope is the 18th. The basic control loops are listed as below: (1) Fresh feed flowrate is flow controlled. (2) The distillate flowrate of HPC is proportional to the feed flowrate. (3) The reflux ratio of HPC is fixed. (4) The pressure of HPC is controlled by manipulating the heat removal rate of the auxiliary condenser, while the pressure of LPC is controlled by manipulating the heat removal rate of the


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condenser. (5) The reflux drum level of HPC is controlled by manipulating the makeup flowrate, while the reflux drum level of LPC is controlled by manipulating the distillate flowrate. (6) The base levels of both columns are controlled by manipulating the bottom flowrate. The temperature control loops are listed as below: (1) The temperature of stage 38 in HPC (T38) is controlled by manipulating the reboiler duty. (2) The temperature of stage 18 in LPC (T18) is controlled by manipulating the reflux ratio. (3) The temperature of feed before entering HPC (TF) is controlled by manipulating the heat removal rate of feed pre-heater. (4) The temperature of recycle stream before entering HPC (TS) is controlled by manipulating the heat removal rate of recycle stream pre-heater. The T38 controller is a PID (proportion, integral, derivative) controller to resist the possible oscillating. All of the other controllers are PI (proportion and integral) controllers. Relay-feedback tests are run on the four temperature controllers to achieve the ultimate gains and periods. Then, Ziegler-Nichols turning is applied to T38 to obtain the gain KC, integral time constant τI and derivative time constant τD. Tyreus−Luyben turning is applied to other temperature controllers to obtain the gain KC and integral time constant τI. Because of the existence of measurement and actuator lags in any real physical system, a 1-min dead time is inserted in the temperature control loops. All flow controllers are PI controllers with the normal settings: KC = 0.5 and τI = 0.3 min. For the level controllers, only a proportional function is employed with KC equal to 2. Details of tuning and setting are covered in Luyben’s book.26



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In this partially heat-integrated process, the reboiler duty of LPC is completely provided by the condenser of HPC. An auxiliary condenser is applied to cool down the excessive heat of HPC vapor distillate. The “flowsheet equations” function is employed to achieve this partial heat integration, which can be found in Supporting Information. Figure 15 shows the temperature control structure and controller faceplate. The tuning parameters of these temperature controllers can be found in Supporting Information. The dynamic performances are tested by making 20% disturbances in the feed flowrate and composition. Both of feed flowrate and composition disturbances are introduced at the time equal to 0.2 h. Figure 16 gives the dynamic responses for feed flowrate disturbance. The results showed that under -20% feed flowrate disturbance, both product purities return to their set points quickly after large but narrow transit deviation, especially for methanol product purity. Under +20% feed flowrate disturbance, toluene product purity can return fairly close to its set points after large but narrow transit deviation, but methanol product purity decreases largely to 98.2%. Meanwhile, for both cases, T38 features intensive periodic fluctuation. Figure 17 gives the dynamic responses for feed composition disturbance. The results showed that under -20% feed composition disturbance, both product purities can be maintained fairly close to their set points while large but narrow transit deviation of toluene product purity can be observed. However, under +20% feed composition disturbance, although toluene product purity can be hold after some fluctuations, methanol product purity decreases largely to 99.2% with very large transit deviation. Meanwhile, for both cases, T38 features periodic fluctuation.


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These results indicate that robust control can’t be achieved. The reason is mainly due to the shape of temperature profile. This ‘reversed L’ style temperature profile limits the controlled temperature variable to single one T38. And the key factor to hold the methanol product purity is to prevent toluene excess in the distillate of HPC. This temperature profile can be divided into two distinctive sections. The temperature profile of upper section between stage 1 and stage 34 has relatively flat trend, while that of the lower section between stage 35 and 40 features dramatically increasing shape. The location of stage 38 in the lower section makes it unable to become an effective controller point to maintain the temperature profile of the upper section. Therefore, it’s difficult to control the mole fraction of toluene in the distillate of HPC. When the process of reboiler duty controlling T38 is proceeding, the corresponding change of the temperature and composition in the upper section will in turn influence T38. However, as holding T38 can’t be effective to maintain the temperature profile of the upper section, it isn’t able to handle the influence from the upper section either. In addition, the response of the upper section and the feedback from the upper section to T38 have time lag. As a result, the reboiler duty can’t make T38 remain stable but show intensive periodic fluctuation all the time. Actually, in the beginning only Tyreus−Luyben turning was applied to get the PI parameters of T38. But it failed to handle the disturbances with large periodic fluctuations of T38 and reboiler duty. Retuning these parameters didn’t work. Therefore derivative controller is introduced to resist the oscillating. The initial PID parameters with Ziegler-Nichols turning can hold the disturbances basically but still with intensive periodic fluctuations of T38 and reboiler duty. After retuning many times, the fluctuations still can’t be avoided. The parameters adopted in this paper are the best ones we have tried.



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3.2. Dynamic Control of PHI-PSD with OP of HPC at 10 atm. The basic control loops are similar as above. The temperature profiles of LPC are shown in Figure 18. The shape of the temperature profile of HPC indicates two temperature control points: the temperatures of stage 22 and 33, respectively. The location of the stage in LPC with the largest slope is the 18th. The temperature control loops are listed as below: (1) The temperature of stage 33 in HPC (T33) is controlled by manipulating the reboiler duty. (2) The temperature of stage 22 in HPC (T22) is controlled by manipulating the heat removal rate of feed pre-heater. (3) The temperature of stage 18 in LPC (T18) is controlled by manipulating the reflux ratio. (4) The temperature of recycle stream before entering HPC (TS) is controlled by manipulating the heat removal rate of recycle stream pre-heater. All of the controllers are PI (proportion and integral) controllers. Relay-feedback tests are run on the four temperature controllers to achieve the ultimate gains and periods. Then, Tyreus−Luyben turning is applied to these temperature controllers to obtain the gain KC and integral time constant τI. The temperature control structure and controller faceplate are shown in Figure 19. The “flowsheet equations” and the tuning parameters of these temperature controllers can be found in Supporting Information. Figure 20 gives the dynamic responses for feed flowrate disturbance. The results showed that under -20% feed flowrate disturbance, both product purities can return to their set points quickly while large but narrow transit deviation of methanol product purity can be observed. Under +20% feed flowrate


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disturbance, toluene product purity can be hold fairly close to its set points after large but narrow transit deviation, while a small negative offset (about -0.5 mol%) of methanol product purity maintains under the new stable steady. Figure 21 gives the dynamic responses for feed composition disturbance. Robust control can be achieved with both product purities maintained close to their set points. Note that under +20% feed composition disturbance, a small negative offset (about -0.5 mol%) of methanol product purity maintains under the new stable steady. Comparing with PHI-PSD with OP of HPC at 12 atm, these results reveal that two temperature control points of HPC make the dynamic controllability of the process robust. 3.3. Dynamic Control of FHI-PSD with OP of HPC at 10 atm. The basic control loops are similar as above. The temperature profiles of HPC and LPC are shown in Figure 22. Two temperature control points of HPC are selected: the temperatures of stage 23 and 33, respectively. The location of the stage in LPC with the largest slope is the 10th. For FHI-PSD, pressure compensated temperature control is widely used to obtain robust controllability.4,29,30 The bubble points of liquid phase on stage 23 and 33 of HPC are investigated at 9-12 bar. The details can be referenced in supporting information. T23 is deemed as a linear function of pressure with a slope of 4.4278. And T33 is deemed as a linear function of pressure with a slope of 5.5320. The “flowsheet equations” and the tuning parameters of these temperature controllers can be found in Supporting Information. The temperature control loops are listed as below:



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Page 18 of 52

(1) The pressure compensated temperature of stage 33 in HPC (TP33) is controlled by manipulating the reboiler duty. (2) The pressure compensated temperature of stage 23 in HPC (TP23) is controlled by manipulating the heat removal rate of feed pre-heater. (3) The temperature of stage 10 in LPC (T10) is controlled by manipulating the reflux ratio. (4) The temperature of recycle stream before entering HPC (TS) is controlled by manipulating the heat removal rate of recycle stream pre-heater. All of the controllers are PI (proportion and integral) controllers. Relay-feedback tests are run on the four temperature controllers to achieve the ultimate gains and periods. Then, Tyreus−Luyben turning is applied to these temperature controllers to obtain the gain KC and integral time constant τI. The temperature control structure and controller faceplate are shown in Figure 23. The tuning parameters of these temperature controllers can be found in Supporting Information. Figure 24 gives the dynamic responses for feed flowrate disturbance. The results showed that under -20% feed flowrate disturbance, both product purities can return to their set points quickly while large but narrow transient deviation of methanol product purity can be observed. Under +20% feed flowrate disturbance, toluene product purity can return to its set points quickly after large but narrow transient deviation, but methanol product purity decreases largely to 98.7%. Figure 25 gives the dynamic responses for feed composition disturbance. Robust control can be achieved with both product purities maintained close to their set points. Note that under +20% feed composition disturbance, a small negative offset (about -0.5 mol%) of methanol product purity maintains under the new stable steady.


Page 19 of 52

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Although this control structure can resist feed composition disturbance well, it can’t resist feed flowrate disturbance effectively. Therefore, the dynamic controllability of FHI-PSD is inferior to that of PHI-PSD. 4. CONCLUSION In this article, PSD for separating pressure-insensitive minimum boiling azeotrope is investigated by demonstrating the separation of methanol/toluene using chloroform as a light entrainer. Both partially and fully heat-integrated PSD processes are implemented. Conventional and partially heat-integrated ED processes are also introduced. Basing on TAC, the optimal designs of PSD and ED processes are compared. The results show the optimal PSD process has 5.39% reduction of TAC and 8.32% energy saving compared to the optimal ED process. For the purpose of energy saving, pre-heater is applied in the PSD process, which reveals that the enthalpic state of feed largely influences not only the energy consumption but also the dynamic controllability. The dynamic performances of three processes are explored including both partially and fully heat-integrated configurations. The economic optimal PSD process shows weak controllability due to the limitation of the temperature profile which is determined by the enthalpic state of feed. However, robust control can be achieved under another superior design. Therefore, this extended PSD has the benefit of being applicable to separate pressure-insensitive minimum boiling azeotrope, at least for methanol/toluene mixture.

Supporting Information (1) Binary interaction parameters of Aspen Plus UIQUAC model; (2) Details of calculating TAC; (3) Iteration procedures of optimization; (4) Controller tuning parameters; (5) Calculation of the slope of the



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

linear function of temperature vs. pressure; (6) Flowsheet equations in control strategies.

NOMENCLATURE B = bottom flowrate C-ED = conventional extractive distillation D = distillate flowrate ED = extractive distillation EDC = extractive distillation column ERC = entrainer recovery column F = feed flowrate FCI = fixed capital investment FHI-PSD = fully heat-integrated pressure-swing distillation HPC = high-pressure column ir = fixed capital recovery rate ID = column diameter KC = gain coefficient LPC = low-pressure column NF = feeding location of fresh feed NFS = feeding location of entrainer NT = total number of stages OC = operating cost


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OP = operating pressure P = pressure PHI-ED = partially heat-integrated extractive distillation PHI-PSD = partially heat-integrated pressure-swing distillation PSD = pressure-swing distillation Q = heat exchanger duty QC = condenser duty QH = pre-heater duty QR = reboiler duty RCM = residue curve map RR = reflux ratio S = recycle stream flowrate T = stage temperature TAC= total annual cost TF = temperature of feed before entering high pressure column TS = temperature of recycle stream before entering high pressure column x = mole fraction τI = integral time constant τD = derivative time constant

REFERENCES



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 52

(1) Luyben, W. L. Comparison of Pressure-Swing and Extractive-Distillation Methods for Methanol-Recovery Systems in the TAME Reactive-Distillation Process. Ind. Eng. Chem. Res. 2005, 44, 5715-5725. (2) Luyben, W. L. Comparison of Extractive Distillation and Pressure-Swing Distillation for Acetone−Methanol Separation. Ind. Eng. Chem. Res. 2008, 47, 2696-2707. (3) Luyben, W. L. Comparison of extractive distillation and pressure-swing distillation for acetone/chloroform separation. Comput. Chem. Eng. 2013, 50, 1-7. (4) Luo, H.; Liang, K.; Li, W.; Li, Y.; Xia, M.; Xu, C. Comparison of Pressure-Swing Distillation and Extractive Distillation Methods for Isopropyl Alcohol/Diisopropyl Ether Separation. Ind. Eng. Chem. Res. 2014, 53, 15167-15182. (5) Lladosa, E.; Montón, J. B.; Burguet, M. C. Separation of di-propyl ether and n-propyl alcohol by extractive distillation and pressure swing distillation: Computer simulation and economic optimization. Chem. Eng. Proc. 2011, 50, 1266-1274. (6) Muñoz, R.; Montón, J. B.; Burguet, M. C.; de la Torre, J. Separation of isobutyl alcohol and isobutyl acetate by extractive distillation and pressure-swing distillation: Simulation and optimization. Sep. Pur. Technol. 2006, 50, 175-183. (7) Modla, G.; Lang, P. Removal and Recovery of Organic Solvents from Aqueous Waste Mixtures by Extractive and Pressure Swing Distillation. Ind. Eng. Chem. Res. 2012, 51, 11473-11481. (8) Luyben, W. L. Design and Control of a Fully Heat-Integrated Pressure-Swing Azeotropic Distillation System. Ind. Eng. Chem. Res. 2008, 47, 2681-2695. (9) Yu, B.; Wang, Q.; Xu, C. Design and Control of Distillation System for Methylal/Methanol


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Separation. Part 2: Pressure Swing Distillation with Full Heat Integration. Ind. Eng. Chem. Res. 2012, 51, 1293-1310. (10) Zhu, Z.; Wang, L.; Ma, Y.; Wang, W.; Wang, Y. Separating an azeotropic mixture of toluene and ethanol via heat integration pressure swing distillation. Comput. Chem. Eng. 2015, 76, 137-149. (11) Phimister, J. R.; Seider, W. D. Semicontinuous, Pressure-Swing Distillation. Ind. Eng. Chem. Res. 2000, 39, 122-130. (12) Repke, J. U.; Klein, A.; Bogle, D.; Wozny, G. Pressure-Swing Batch Distillation for Homogenous Azeotropic Separation. Chem. Eng. Res. Des. 2007, 85, 492-501. (13) Modla, G.; Lang, P. Feasibility of new pressure-swing batch distillation methods. Chem. Eng. Sci. 2008, 63, 2856-2874. (14) Modla, G.; Lang, P.; Denes, F. Feasibility of separation of ternary mixtures by pressure-swing batch distillation. Chem. Eng. Sci. 2010, 65, 870-881 (15) Luyben, W. L. Methanol/Trimethoxysilane Azeotrope Separation Using Pressure-Swing Distillation. Ind. Eng. Chem. Res. 2014, 53, 5590-5597. (16) Luyben, W. L. Control of a Heat-Integrated Pressure-Swing Distillation Process for the Separation of a Maximum-Boiling Azeotrope. Ind. Eng. Chem. Res. 2014, 53, 18042-18053. (17) Li, R.; Ye, Q.; Suo, X.; Dai, X.; Yu, H. Heat-Integrated Pressure-Swing Distillation Process for Separation of a Maximum-Boiling Azeotrope Ethylenediamine-Water. Chem. Eng. Res. Des. 2016, 105, 1-15. (18) Knapp, J. P.; Doherty, M. F. A New Pressure-Swing-Distillation Process for Separating Homogeneous Azeotropic Mixtures. Ind. Eng. Chem. Res. 1992, 31, 346-357.



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(19) Li, W.; Shi, L.; Yu, B.; Xia, M.; Luo, J.; Shi, H.; Xu, C. New Pressure-Swing Distillation for Separating Pressure-Insensitive Maximum Boiling Azeotrope via Introducing a Heavy Entrainer: Design and Control. Ind. Eng. Chem. Res. 2013, 52, 7836-7853. (20) Modla, G. Energy saving methods for the separation of a minimum boiling point azeotrope using an intermediate entrainer. Energy 2013, 50, 103-109. (21) Ma, J.; Li, W.; Ni, C.; Li, Y.; Huang, S.; Shen, C.; Xu, C. Investigation of distillation systems using heavy or intermediate entrainers for separating toluene−methanol: process economics and control. J. Chem. Technol. Biotechnol. 2016, 91, 2111-2114. (22) Huang, S.; Li, W.; Li, Y.; Ma, J.; Shen, C.; Xu, C. Process Assessment of Distillation Using Intermediate Entrainer: Conventional Sequences to the Corresponding Dividing-Wall Columns. Ind. Eng. Chem. Res. 2016, 55, 1655-1666. (23) Luyben, W. L. Improved design of an extractive distillation system with an intermediate-boiling solvent. Sep. Purif. Technol. 2015, 156, 336-347. (24) Foucher, E. R.; Doherty, M. F.; Malone, M. F. Automatic Screening of Entrainers for Homogeneous Azeotropic Distillation. Ind. Eng. Chem. Res. 1991, 30, 760-772. (25) Gmehling, J.; Menke, J.; Krafczyk, J.; Fischer, K. Azeotropic Data. Wiley-VCH: Weinheim, Germany, 2004. (26) Luyben, W. L. Distillation Design and Control Using Aspen Simulation; John Wiley & Sons: New York, 2006. (27) Douglas, J. M. Conceptual Design of Chemical Processes; McGraw Hill: New York, 1998. (28) Turton, R.; Bailie, R. C.; Whiting, W. B.; Shaeiwitz, J. A. Analysis, Synthesis and Design of


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Chemical Processes; Prentice Hall: New York, 2009. (29) Wang, Y.; Zhang, Z.; Zhang, H.; Zhang, Q. Control of Heat Integrated Pressure-Swing-Distillation Process for Separating Azeotropic Mixture of Tetrahydrofuran and Methanol. Ind. Eng. Chem. Res. 2015, 54, 1646-1655. (30) Wang, Y.; Zhang, Z.; Zhao, Y.; Liang, S.; Bu, G. Control of Extractive Distillation and Partially Heat-Integrated Pressure-Swing Distillation for Separating Azeotropic Mixture of Ethanol and Tetrahydrofuran. Ind. Eng. Chem. Res. 2015, 54, 8533-8545.



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Page 26 of 52

Table 1. Case Study of the Optimal OP of HPC of PHI-PSD Variables

Case 1

Case 2

Case 3

P1 (atm)

8

10

12

NT1

30

35

40

NF1/NFS

26/14

32/20

38/26

RR1

0.400

0.352

0.367

ID1 (m)

0.852

0.747

0.687

QR1 (kW)

147.78

193.46

319.42

P2 (atm)

0.8

0.8

0.8

NT2

30

30

30

NF2

12

11

11

RR2

0.829

0.939

1.075

ID2 (m)

1.306

1.228

1.173

QC2 (kW)

-1714.82

-1518.54

-1383.74

Q (kW)

1332.40

1132.38

997.20

Qaux (kW)

-379.46

-304.33

-292.64

Recycle Stream

S (kmol/hr)

105.39

87.96

74.96

Pre-Heater (Feed)

QH1 (kW)

1298.57

1189.81

1083.33

Pre-Heater (Entrainer)

QH3 (kW)

1085.69

922.00

796.85

FCI (k$/year)

1264.99

1227.18

1257.93

OC (k$/year)

1042.61

956.08

927.41

TAC (k$/year)

1422.11

1324.23

1304.79

HPC

LPC

Heat Exchanger


Page 27 of 52

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Table 2. Case Study of the Optimal OP of HPC of FHI-PSD Variables

Case 1

Case 2

Case 3

P1 (atm)

8

10

12

NT1

30

35

40

NF1/NFS

26/14

32/20

38/26

RR1

0.401

0.352

0.367

ID1 (m)

0.850

0.746

0.687

QR1 (kW)

147.91

194.11

319.41

P2 (atm)

0.8

0.8

0.8

NT2

19

22

21

NF2

7

8

8

RR2

1.241

1.331

1.517

ID2 (m)

1.448

1.349

1.295

QC2 (kW)

-2092.96

-1825.50

-1678.90

Heat Exchanger

Q (kW)

1708.64

1437.14

1289.84

Recycle Stream

S (kmol/hr)

104.96

87.96

74.96

Pre-Heater (Feed)

QH1 (kW)

1298.57

1189.81

1083.33

Pre-Heater (Entrainer)

QH3 (kW)

1081.29

921.93

796.94

FCI (k$/year)

1218.86

1208.20

1234.84

OC (k$/year)

1040.98

956.41

927.46

TAC (k$/year)

1406.64

1318.87

1297.92

HPC

LPC



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Page 28 of 52

Table 3. Economic Comparison of the Optimal Designs Item

Saving Gain

OP of HPC OC (k$/year)

FCI (k$/year)

TAC (k$/year)

OC

FCI

TAC

10

956.08

1227.18

1324.23

-5.49%

2.21%

-3.47%

12

927.41

1257.93

1304.79

-8.32%

4.77%

-4.88%

10

956.41

1208.20

1318.87

-5.46%

0.63%

-3.86%

12

927.46

1234.84

1297.92

-8.32%

2.85%

-5.39%

C-ED

-

1011.60

1200.64

1371.79

-

-

-

PHI-ED

-

928.26

1650.01

1423.26

-8.24%

37.43%

3.75%

PHI-PSD

FHI-PSD

a

: Saving gain is calculated basing on the corresponding item of C-ED.


Page 29 of 52

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Recycle Stream Ethanol/Water/Acetone Ternary Mixture

Entrainer Makeup

F

Reflux

LPC

D1

HPC

Ethanol/Water Mixture

Water

Ethanol

(a)

Figure 1. (a) Recycle Stream Phenol/Acetophenone Binary Mixture

Cyclohexanone Entrainer Makeup

F

Phenol

Reflux

LPC

B1

HPC

Phenol/Cyclohexanone Mixture

(b)

Figure 1. (b) Figure 1. (a) PSD process for separating ethanol/water using acetone as the entrainer. (b) PSD process for separating phenol/cyclohexanone using acetophenone as the entrainer.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 2. (a)

Figure 2. (b)


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Page 31 of 52

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Figure 2. (c) Figure 2. (a) RCM of methanol/toluene/chloroform system at 1 atm. (b) RCM of methanol/toluene/chloroform system at 10 atm. (c) Feasibility analysis of PSD for methanol/toluene separation using chloroform as the entrainer.



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Page 32 of 52

Recycle Stream

Reflux

Entrainer Makeup

HPC

D1

LPC

F Methanol/Toluene Mixture

Toluene

Methanol

Figure 3. The schematic flowsheet of this PSD process for separating methanol/toluene.


Page 33 of 52

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Make-up 0.033 kmol/h Chloroform

P1=10.00 atm 131.99 ¡ æ

P2=0.80 atm 48.15 ¡ æ -1518.54 kW

D1 137.96 kmol/h 0.5695 methanol 0.0003 toluene 0.4302 chloroform mol %

2 139.61 ¡ æ 922.00 kW

2

11

50 ¡ æ 100.00 kmol/h 0.5 methanol 0.5 toluene mol %

ID1=0.747 m RR1=0.352

20

32

F 174.74 ¡ æ 1189.81 kW

50.01 kmol/h 5.217e-4 methanol 0.999 toluene 4.783e-4 chloroform mol % B1

35

D2 87.93 kmol/h 0.3251 methanol 3.857e-8 toluene 0.6749 chloroform mol %

ID2=1.228 m RR2=0.939 Auxiliary Condenser 30 304.33 kW

50.02 kmol/h 0.999 methanol 8.273e-4 toluene 1.727e-4 chloroform mol % B2

64.38 ¡ æ 1132.38 kW

218.05 ¡ æ 193.46 kW

Figure 4. Optimal flowsheet of PHI-PSD with OP of HPC at 10 atm. Make-up 0.041 kmol/h Chloroform

P1=12.00 atm 139.73 ¡ æ

P2=0.80 atm 48.15 ¡ æ -1383.74 kW

D1 124.94 kmol/h 0.5939 methanol 0.0003 toluene 0.4058 chloroform mol %

2 148.57 ¡ æ 796.85 kW

2

11

50 ¡ æ 100.00 kmol/h 0.5 methanol 0.5 toluene mol % F 174.00 ¡ æ 1083.33 kW

26

ID1=0.687 m RR1=0.367

38


40


ID2=1.173 m RR2=1.075 Auxiliary Condenser 30 292.64 kW

64.37 ¡ æ 997.20 kW

229.39 ¡ æ 319.42 kW

Figure 5. Optimal flowsheet of PHI-PSD with OP of HPC at 12 atm.




1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Make-up 0.033 kmol/h Chloroform

P1=10.00 atm 131.99 ¡ æ D1 137.95 kmol/h 0.5699 methanol 0.0003 toluene 0.4298 chloroform mol %

2 139.58 ¡ æ 921.93 kW

Page 34 of 52

P2=0.80 atm 48.15 ¡ æ -1825.50 kW 2

8


ID1=0.746 m RR1=0.352

20

32


35


ID2=1.349 m RR2=1.331

22


62.98 ¡ æ 1437.14 kW

218.11 ¡ æ 194.11 kW

Figure 6. Optimal flowsheet of FHI-PSD with OP of HPC at 10 atm. Make-up 0.036 kmol/h Chloroform

P1=12.00 atm 139.73 ¡ æ D1 124.95 kmol/h 0.5938 methanol 0.0003 toluene 0.4059 chloroform mol %

2 148.57 ¡ æ 796.94 kW

P2=0.80 atm 48.15 ¡ æ -1678.90 kW 2

8


26

ID1=0.687 m RR1=0.367

38


40

ID2=1.295 m RR2=1.517

21

62.79 ¡ æ 1289.84 kW

229.39 ¡ æ 319.41 kW

Figure 7. Optimal flowsheet of FHI-PSD with OP of HPC at 12 atm.




Page 35 of 52

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


P1=1.00 atm 64.52 ¡ æ -589.95kW

Make-up 0.027 kmol/h Aniline

P2=1.00 atm 110.18 ¡ æ -724.73 kW

D1

2

D2

2

50.001 kmol/h 4.777e-4 methanol 0.999 toluene 5.223e-4 aniline mol %

50.026 kmol/h 0.999 methanol 9.868e-4 toluene 1.326e-5 aniline mol %

6

ID2=0.942 m RR2=0.558

ID1=1.238 m RR1=0.208

50.0 ¡ æ 100.00 kmol/h 0.5 methanol 0.5 toluene mol %

16

38 F

139.971 kmol/h 1.706e-4 methanol 0.3569 toluene 0.6429 aniline mol %

41

50.0 ¡ æ -761.10 kW

89.970 kmol/h trace methanol 0.0001 toluene 0.9999 aniline mol %

26

B2

B1

190.11 ¡ æ 825.95 kW

145.30 ¡ æ 1393.30 kW

Figure 8. The optimal flowsheet of C-ED. P1=0.548 atm 49.98 ¡ æ -687.15kW

Make-up 0.024 kmol/h Aniline

D1

2 6

2 50.02 kmol/h 0.999 methanol 9.993e-4 toluene 7.311e-7 aniline mol %

D2

50.00 kmol/h 5.839e-4 methanol 0.999 toluene 4.161e-4 aniline mol % ID2=1.013 m RR2=0.630

ID1=1.300 m RR1=0.369

50.0 ¡ æ 100.00 kmol/h 0.5 methanol 0.5 toluene mol %

15

46 F

P2=2.72 atm 149.35 ¡ æ -701.69 kW

50

130.72 ¡ æ 609.57 kW

149.35 ¡ æ 701.69 kW

145.99 kmol/h 0.0002 methanol 0.3422 toluene 0.6576 aniline mol % B1

Figure 9. The optimal flowsheet of PHI-ED.


50.0 ¡ æ -1078.36 kW

95.99 kmol/h trace methanol 0.0001 toluene 0.9999 aniline mol %

28

B2

228.93 ¡ æ 1328.34 kW


1.0

220

Methanol Toluene CHCl3

0.8 Liquid Mole Fraction

T (°C)

200 180 160 140 120

0

5

10

15

20

25

30

35

0.6 0.4 0.2 0.0

0

5

10

Stage

0.25

0.75

ene To lu

0.50

0.50

0.75

0.2

5

10

35

Liquid Composition Vapor Composition

0.4

0

30

0.00 1.00


0.6

0.0

25

15

20

25

30

35

1.00 0.00

l

Vapor Mole Fraction

20 Stage

1.0 0.8

15

no tha Me

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 52

0.25

0.25

Stage

0.50 Chloroform

0.75

0.00 1.00

Figure 10. Temperature profile and composition profiles of the optimal PHI-PSD with OP of HPC at 10 atm and the vapor fraction of feed at 0.85.


Page 37 of 52

1.0 220


0.8 Liqiud Mole Fraction

T (°C)

200 180 160 140 120

0

5

10

15

20

25

30

35

0.6 0.4 0.2 0.0

0

5

10

Stage

0.25

0.75

e lue n To

0.50

0.50

0.75

0.2

5

10

35


0.4

0

30

0.00 1.00


0.6

0.0

25

15

20

25

30

35

1.00 0.00

ol

Vapor Mole Fraction

20 Stage

1.0 0.8

15

n tha Me

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.25

0.25

Stage

0.50 Chloroform

0.75

0.00 1.00




1.0

220


240

T (°C)

200 180 160


0.6 0.4 0.2

140 0

10

20

30

40

0.0

0

10

Stage

0.25

0.75

l ue ne To

0.50

0.50

0.75

0.2

10

20

30

40

1.00 0.00

l

Vapor Mole Fraction


0.4

0

40

0.00 1.00


0.6

0.0

30

Stage

1.0 0.8

20

no t ha Me

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 52

0.25

0.25

Stage

0.50 Chloroform

0.75

0.00 1.00



Page 39 of 52

1.0

220


240

T (°C)

200 180 160


0.6 0.4 0.2

140 0

10

20

30

40

0.0

0

10

1.0

0.25

0.75

e lue n To

0.50

ano

0.50

0.75

0.2

10

20

30

40

1.00 0.00

l

Vapor Mole Fraction


0.4

0

40

0.00 1.00


0.6

0.0

30

Stage

Stage

0.8

20

th Me

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.25

0.25

Stage

0.50 Chloroform

0.75

0.00 1.00




66 64 62 60 58 T (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 52

56 54 52 50 48 46

0

5

10

15

20

25

30

Stage

Figure 14. Temperature profile of LPC of PHI-PSD with OP of HPC at 12 atm.


Page 41 of 52

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 15. Overall control strategy of PHI-PSD with OP of HPC at 12 atm.


1.00

0.995

0.99

Mole Fraction of Methanol

1.000

+20% Flowrate - 20% Flowrate

0.990 0.985 0.980 0.975 0.970


0.97 0.96 0.95 0.94

0

5

10

15

20

25

0.93

30

0

5

10

30


450 QR1 (kw)

200

T38 (°C)

25

500


210

190 180

400 350 300

170

250

160 0

5

10

15

20

25

200

30

0

5

10

Time (hr)

15

20

25

30

Time (hr) 1400

188 184

1300 +20% Flowrate - 20% Flowrate

180

1200 QHF (kw)

176 172


1100 1000

168

900

164 160

20

550

220

150

15 Time (hr)

230

TF (°C)

Page 42 of 52

0.98

Time (hr)

0

5

10

15

20

25

800

30

0

5

10

Time (hr)

15

20

25

30

Time (hr)

61

2.0

60

1.8


1.6


59

1.4 RR2

T18 (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Mole Fraction of Toluene


58 57

1.0

56 55

1.2

0.8 0

5

10

15

20

25

30

0.6

0

5

Time (hr)

10

15

20

25

30

Time (hr)

Figure 16. Dynamic responses to feed flowrate disturbance of PHI-PSD with OP of HPC at 12 atm.


1.000

0.999

0.995 Mole Fraction of Methanol

1.000

0.998 +20% Composition - 20% Composition

0.997 0.996 0.995 0.994 0.993 0.992

0

5

10

15

20

25

0.990

0.980 0.975 0.970 0.965

30

+20% Composition - 20% Composition

0.985

0

5

10

Time (hr)


QR1 (kw)

T38 (°C)

30

400

190 180

300

170

250

0

5

10

15

20

25

200

30


350

0

5

10

Time (hr)

15

20

25

30

Time (hr) 1250

188 +20% Composition - 20% Composition

184

1200

180

1150

176

QF (kw)

TF (°C)

25

450

200

172


1100 1050

168

1000

164 160

20

500

210

160

15 Time (hr)

220

0

5

10

15

20

25

950

30

0

5

10

Time (hr)

15

20

25

30

Time (hr) 1.6

60

1.5


59

1.4 1.3

58 RR2

T18 (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



Page 43 of 52

57


1.2 1.1 1.0

56

0.9 55

0

5

10

15

20

25

30

0.8

0

5

Time (hr)

10

15

20

25

30

Time (hr)

Figure 17. Dynamic responses to feed composition disturbance of PHI-PSD with OP of HPC at 12 atm.



66 64 62 60 58

T (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 52

56 54 52 50 48 46 0

5

10

15

20

25

30

Stage

Figure 18. Temperature profile of LPC of PHI-PSD with OP of HPC at 10 atm.


Page 45 of 52

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 19. Overall control strategy of PHI-PSD with OP of HPC at 10 atm.


1.000

0.999


1.000

0.998 +20% Flowrate - 20% Flowrate

0.997 0.996 0.995 0.994

0

5

10

15

20

25

0.985


0.980 0.975 0.970 0.965

30

0

5

10

500

210

450

QR1 (kw)

T33 (°C)

25

30


350


180 170

300 250 200

160

150 0

5

10

15

20

25

100

30

0

5

10

Time (hr)

15

20

25

30

Time (hr)

190

1800

180

1600 +20% Flowrate - 20% Flowrate QF (kw)

170 160


1200

150 1000

140 130

20

400

200

T22 (°C)

15 Time (hr)

220

150

Page 46 of 52

0.990

Time (hr)

0

5

10

15

20

25

800

30

0

5

10

Time (hr)

15

20

25

30

Time (hr)

61

1.6

60

1.4 +20% Flowrate - 20% Flowrate

59

1.2

58 RR2

T18 (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



57 56


1.0 0.8

55 0.6

54 53

0

5

10

15

20

25

30

0.4

0

5

Time (hr)

10

15

20

25

30

Time (hr)

Figure 20. Dynamic responses to feed flowrate disturbance of PHI-PSD with OP of HPC at 10 atm.


Page 47 of 52

1.000 0.999 Mole Fraction of Methanol


0.9995 0.9990 0.9985 0.9980 0.9975

0

5

10

15

20

25

0.998

0.996 0.995 0.994 0.993

30


0.997

0

5

10

Time (hr)

20

25

30

210

202

200 +20% Composition - 20% Composition QR1 (kw)

200 T33 (°C)

15 Time (hr)

204

198


180

196 170

194 192

0

5

10

15

20

25

160

30

0

5

10

Time (hr)

15

20

25

30

Time (hr)

162

1280


158

1260

156

1240

154 QF (kw)

T22 (°C)

152 150


1220 1200

148 146

1180

144 142

0

5

10

15

20

25

1160

30

0

5

10

Time (hr)

15

20

25

30

Time (hr)

58.8

1.3

58.4

1.2


58.0

1.1

57.6 RR2


1.0000

T18 (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


57.2 56.8


1.0 0.9

56.4 0.8

56.0 55.6

0

5

10

15

20

25

30

0.7

0

5

Time (hr)

10

15

20

25

30

Time (hr)

Figure 21. Dynamic responses to feed composition disturbance of PHI-PSD with OP of HPC at 10 atm.



(a) 240

(b) 66 64

220

62 60

200 T (°C)

58 T (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 48 of 52

180 160

56 54 52 50

140

48 120

0

5

10

15

20

25

30

35

46

0

2

4

Stage

6

8

10

12

14

16

18

20

22

Stage

Figure 22. (a) Temperature profile of HPC of FHI-PSD with OP of HPC at 10 atm. (b) Temperature profile of LPC of FHI-PSD with OP of HPC at 10 atm.


Page 49 of 52

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 23. Overall control strategy of FHI-PSD with OP of HPC at 10 atm.



1.00

0.99

Mole Fraction of Methanol


1.00


0.98 0.97 0.96 0.95

0

5

10

15

20

25

0.98 0.96

0.92 0.90 0.88

30


0.94

0

5

10

Time (hr)

20

25

30

450 400


220


350

200

QR1 (kw)

T33 (°C)

15 Time (hr)

240

180

300 250 200

160 150 140

0

5

10

15

20

25

100

30

0

5

10

Time (hr)


25

30

1300 QF (kw)

T23 (°C)

20

1500

180

160

140

120

15 Time (hr)

200


1100

900

0

5

10

15

20

25

700

30

0

5

10

Time (hr)

15

20

25

30

Time (hr) 1.6

58


1.5


57

1.4 56 RR2

T10 (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 50 of 52

1.3

55 1.2 54 53

1.1

0

5

10

15

20

25

30

1.0

0

5

Time (hr)

10

15

20

25

30

Time (hr)

Figure 24. Dynamic responses to feed flowrate disturbance of FHI-PSD with OP of HPC at 10 atm.


Page 51 of 52

1.0000




0.9996

0.9992

0.9988

0.9984

0

5

10

15

20

25

0.997 0.996 0.995 0.994 0.993

30


0.998

0

5

10

Time (hr)

20

25

30

210

210

200 +20% Composition - 20% Composition QR1 (kw)

205 T33 (°C)

15 Time (hr)

215

200

190 +20% Composition - 20% Composition 180

195 170

190 185

0

5

10

15

20

25

160

30

0

5

10

Time (hr)

20

25

30

1320

160

1280 +20% Composition - 20% Composition QF (kw)

156 T23 (°C)

15 Time (hr)

164

152

1240 +20% Composition - 20% Composition 1200

148 1160

144 140

0

5

10

15

20

25

1120

30

0

5

10

Time (hr)

20

25

30

1.7

56.6


56.2

1.5

55.8


RR2

1.4

55.4 55.0

1.3 1.2

54.6

1.1

54.2 53.8

15 Time (hr)

57.0

T10 (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0

5

10

15

20

25

30

1.0

0

5

Time (hr)

10

15

20

25

30

Time (hr)

Figure 25. Dynamic responses to feed composition disturbance of FHI-PSD with OP of HPC at 10 atm.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

For Table of Contents Only


Page 52 of 52

Pressure-Swing Distillation for Separating Pressure-Insensitive

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